Statistics Sunday: My 2019 Reading

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I’ve spent the month of April blogging my way through the tidyverse, while using my reading dataset from 2019 as the example. Today, I thought I’d bring many of those analyses and data manipulation techniques together to do a post about my reading habits for the year.

library(tidyverse)

## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --

## <U+2713> ggplot2 3.2.1     <U+2713> purrr   0.3.3
## <U+2713> tibble  2.1.3     <U+2713> dplyr   0.8.3
## <U+2713> tidyr   1.0.0     <U+2713> stringr 1.4.0
## <U+2713> readr   1.3.1     <U+2713> forcats 0.4.0

## -- Conflicts ---------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

reads2019 <- read_csv("~/Downloads/Blogging A to Z/SaraReads2019_allchanges.csv",
                      col_names = TRUE)

## Parsed with column specification:
## cols(
##   Title = col_character(),
##   Pages = col_double(),
##   date_started = col_character(),
##   date_read = col_character(),
##   Book.ID = col_double(),
##   Author = col_character(),
##   AdditionalAuthors = col_character(),
##   AverageRating = col_double(),
##   OriginalPublicationYear = col_double(),
##   read_time = col_double(),
##   MyRating = col_double(),
##   Gender = col_double(),
##   Fiction = col_double(),
##   Childrens = col_double(),
##   Fantasy = col_double(),
##   SciFi = col_double(),
##   Mystery = col_double(),
##   SelfHelp = col_double()
## )

As you recall, I read 87 books last year, by 42 different authors.

reads2019 %>%
  summarise(Books = n(),
            Authors = n_distinct(Author))

## # A tibble: 1 x 2
##   Books Authors
##   <int>   <int>
## 1    87      42

Using summarise, we can get some basic information about each author.

authors <- reads2019 %>%
  group_by(Author) %>%
  summarise(Books = n(),
            Pages = sum(Pages),
            AvgRating = mean(MyRating),
            Oldest = min(OriginalPublicationYear),
            Newest = max(OriginalPublicationYear),
            AvgRT = mean(read_time),
            Gender = first(Gender),
            Fiction = sum(Fiction),
            Childrens = sum(Childrens),
            Fantasy = sum(Fantasy),
            Sci = sum(SciFi),
            Mystery = sum(Mystery))

Let’s plot number of books by each author, with the bars arranged by number of books.

authors %>%
  ggplot(aes(reorder(Author, desc(Books)), Books)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 90)) +
  xlab("Author")

authors %>%
  mutate(Author = fct_reorder(Author, desc(Author))) %>%
  filter(Books > 1) %>%
  ggplot(aes(reorder(Author, Books), Books)) +
  geom_col() +
  coord_flip() +
  xlab("Author")

authors %>%
  filter(Books > 1) %>%
  ggplot(aes(Author, AvgRating)) +
  geom_col() +
  scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
  ylab("Average Rating")

genre <- reads2019 %>%
  group_by(Fiction, Childrens, Fantasy, SciFi, Mystery) %>%
  summarise(Books = n(),
            AvgRating = mean(MyRating)) %>%
  bind_cols(Genre = c("Non-Fiction",
           "General Fiction",
           "Mystery",
           "Science Fiction",
           "Fantasy",
           "Fantasy Sci-Fi",
           "Children's Fiction",
           "Children's Fantasy"))

genre %>%
  ggplot(aes(reorder(Genre, desc(AvgRating)), AvgRating)) +
  geom_col() +
  scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
  xlab("Genre") +
  ylab("Average Rating")

Based on this plot, my favorite genres appear to be fantasy, sci-fi, and especially books with elements of both. No surprises here.

lowratings <- reads2019 %>%
  filter(MyRating <= 3) %>%
  mutate(Rating = case_when(MyRating == 2 ~ "Hated",
                   MyRating == 3 ~ "Disliked")) %>%
  arrange(desc(MyRating), Author) %>%
  select(Title, Author, Rating)

library(expss)

## 
## Attaching package: 'expss'

## The following objects are masked from 'package:stringr':
## 
##     fixed, regex

## The following objects are masked from 'package:dplyr':
## 
##     between, compute, contains, first, last, na_if, recode, vars

## The following objects are masked from 'package:purrr':
## 
##     keep, modify, modify_if, transpose

## The following objects are masked from 'package:tidyr':
## 
##     contains, nest

## The following object is masked from 'package:ggplot2':
## 
##     vars

as.etable(lowratings, rownames_as_row_labels = FALSE)

reads2019 <- reads2019 %>%
  mutate(MyRating = replace(MyRating,
                            MyRating == 2, 1),
         MyRating = replace(MyRating,
                            Title == "Herself Surprised", 2))

lowratings <- reads2019 %>%
  filter(MyRating <= 2) %>%
  mutate(Rating = case_when(MyRating == 1 ~ "Hated",
                   MyRating == 2 ~ "Disliked")) %>%
  arrange(desc(MyRating), Author) %>%
  select(Title, Author, Rating)

library(expss)

as.etable(lowratings, rownames_as_row_labels = FALSE)

topbygenre <- reads2019 %>%
  left_join(genre, by = c("Fiction","Childrens","Fantasy","SciFi","Mystery")) %>%
  select(-Books, -AvgRating) %>%
  filter(MyRating == 5)

topbygenre %>%
  ggplot(aes(fct_infreq(Genre))) +
  geom_bar() +
  scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
  xlab("Genre") +
  ylab("Books")

genre %>%
  ggplot(aes(reorder(Genre, desc(AvgRating)), AvgRating, label = Books)) +
  geom_col() +
  scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
  xlab("Genre") +
  ylab("Average Rating") +
  geom_text(aes(x = Genre, y = AvgRating-0.25), size = 5,
                color = "white")

genre %>%
  filter(Books > 2) %>%
  ggplot(aes(reorder(Genre, desc(AvgRating)), AvgRating, label = Books)) +
  geom_col() +
  scale_x_discrete(labels=function(x){sub("\\s", "\n", x)}) +
  xlab("Genre") +
  ylab("Average Rating") +
  geom_text(aes(x = Genre, y = AvgRating-0.25), size = 5,
                color = "white")